Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Initiated by volunteer computing efforts, the computation outsourcing problem can become a compelling application for networked set-top-boxes and mobile devices. In this paper we extend such environments with the ability to provide secure payments in exchange for outsourced CPU cycles. Previous contributions in wired networks have almost exclusively tackled only one side of the problem -- offering incentives for volunteer participation and preventing worker laziness. This makes sense in static environments where reputable outsourcers have little to gain from incorrectly rewarding honest participation. However, this assumption is no longer valid in ad hoc environments, where unique identities are difficult to provide and anyone can outsource computations. In this paper we propose a solution that simultaneously ensures correct remuneration for jobs completed on time and prevents worker laziness. Our solution relies on an offline bank to generate and redeem payments; the bank is oblivious to interactions between outsourcers and workers. In particular, the bank is not involved in job computation or verification. Our experiments show that the solution is efficient: the bank can perform hundreds of payment transactions per second and the overheads imposed on outsourcers and workers are negligible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it